Blue Sage Data Systems
A play we run with Omaha mid-market teams

Good, Better, Best — small AI gains, daily, that compound

Most "I'm behind on AI" anxiety is solved by a different question — not "how do I overhaul this," but "how do I make this 10% better today, and then again tomorrow." The cumulative shift is what changes the work.

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The pattern

Most people approaching AI run one of two scripts in their head: "I'm already behind and I need to overhaul everything," or "I don't even know where to start, so I'll start never." Both end in the same place — nowhere.

The Good-Better-Best play is the alternative. Pick one workflow you do today, and find the version of it that's 10% better with AI in the loop. Use it. Tomorrow, do the same with another workflow. Or do the same workflow 10% better than yesterday.

This compounds. The AI high performers in McKinsey's 2025 data — the ~6% of organizations attributing 5%+ EBIT impact to AI — didn't get there by overhauling everything in a quarter. They got there by being nearly 3x as likely to fundamentally redesign individual workflows over time, one at a time. The compounding small-gain pattern *is* the workflow redesign pattern, run at the level of individual contributors.

The biggest gains in mid-market AI adoption aren't from people using AI in flashy new ways. They're from people using AI for 5 more minutes a day than they did last week. Repeatable, small, doable inside a normal workday.

The play

  1. From "good" to "better" — keep a personal prompt library

    Write down 5–10 prompts you actually use. Reuse them. Tweak them. Save them where you can find them — OneNote, Copilot Prompt Gallery, a notebook. The time saved from not re-inventing prompts compounds faster than any flashy new use case.

  2. From "good" to "better" — paste more context than feels reasonable

    If you're asking AI to help with an email, paste the last three emails in the thread, not just your draft. Context is the cheat code. If you think you're giving the tool too much context, that's probably still not enough.

  3. From "good" to "better" — name the audience

    "Rewrite this for a skeptical CFO" produces wildly different output than "make this better." Tell the tool who it's for. The more specific, the better.

  4. From "better" to "best" — use AI to think, not just to type

    The shift from "do this task for me" to "help me decide / pressure-test / reflect" is where the real value opens up. Most teams don't make this move because they're stuck on AI as a typing accelerator.

  5. From "better" to "best" — let it disagree with you

    "Argue the opposite side of this." "What am I missing?" "What would a skeptical reader push back on?" Produces sharper thinking than any yes-machine pattern. Most underused move in AI, full stop.

  6. From "better" to "best" — debrief the week, not just plan it

    Most people only use AI looking forward. Looking backward is where the pattern recognition lives. Paste in what you worked on this week, ask what you spent too much time on, ask what you avoided. The answer is often surprising.

What changes at 30 / 60 / 90 days

30 days

One person on the team has adopted Good-Better moves and visibly saved time. The team has noticed. Prompt library exists for at least one role.

60 days

Three to five team members are running daily Good-Better practices. A team-shared prompt library exists. Activity is observable in the work product, not just in a usage metric.

90 days

Team workflow has shifted from individual experimentation to shared practice. The next workflow redesign — moving toward Best — is actively in scope. Adoption isn't a project anymore; it's a habit.

When this play applies

Is this just glorified prompt engineering?
Some of the early steps are. The full play is about a posture shift — from AI as typing aid to AI as thinking partner. Prompt engineering gets you to 'better.' The thinking-partner shift is what gets you to 'best.'
Why does this work better than a top-down rollout?
Because it doesn't require permission, infrastructure, or a planning cycle to start. McKinsey's data shows two-thirds of organizations haven't begun scaling AI — most because they're waiting for the perfect rollout plan. Good-Better-Best produces compounding gains while the rollout plan is being written.
Does this work for whole teams or just individuals?
Both. The pattern that works at team level: leaders use AI openly and casually in front of their team — five minutes in a meeting, on real work — and the practice spreads.
What about senior leaders? Different dynamic?
Yes. SHRM 2026 found 73% of directors and above report creativity improvements from AI vs. 65% of individual contributors. Senior leaders are getting more from AI on average — partly because they're using it for thinking work where the gains are larger. The Good-Better-Best play is how individual contributors close that gap.
When should we move from individual practice to coordinated rollout?
When 25–40% of the team is fluently using AI on daily work. Earlier than that, a coordinated rollout fails for lack of internal advocates. Later than that, you start losing the gains to inconsistent practice across roles. The Good-Better-Best play is what produces the early adopters who make the coordinated rollout possible.

Sources

Related

Playbook
good, better, best framework
Prompt
AI interview-me-first template
Prompt
the pressure-test prompt
Prompt
the weekly debrief prompt
Service
role-specific AI training in Omaha
Service
fully managed AI rollout
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